Mueller Matrix Decomposition of Diffuse Reflectance Imaging in Skeletal Muscle

doi:10.1364/AO.48.002625Propagation of polarized light in skeletal muscle is significantly affected by anisotropic muscle structures. To completely characterize muscle polarization properties, we acquired the whole Mueller matrix images of the diffuse reflectance. A polar decomposition algorithm was applied to extract the individual diattenuation, retardance, and depolarization images from the measured Mueller matrix. The decomposed polarization properties in muscle show distinctly different patterns from those obtained in isotropic scattering media. Stretching the prerigor muscle sample induced clear changes in the raw polarization reflectance images. However, muscle stretching induced minimal changes in the decomposed Mueller matrix images.This work was supported in part by National Science Foundation (NSF) grant CBET-0643190

Polarization-sensitive Reflectance Imaging in Skeletal Muscle

We acquired polarization-sensitive reflectance images in freshly excised skeletal muscle samples. The obtained raw images varied depending on the incident and detection polarization states. The Stokes vectors were measured for incident light of four different polarization states, and the whole Mueller matrix images were also calculated. We found that the images obtained in skeletal muscles exhibited different features from those obtained in a typical polystyrene sphere solution. The back-reflected light in muscle maintained a higher degree of polarization along the axis perpendicular to muscle fiber orientation. Our analysis indicates that the unique muscle sarcomere structure plays an important role in modulating the propagation of polarized light in whole muscle

Deep learning assisted jet tomography for the study of Mach cones in QGP

Mach cones are expected to form in the expanding quark-gluon plasma (QGP) when energetic quarks and gluons (called jets) traverse the hot medium at a velocity faster than the speed of sound in high-energy heavy-ion collisions. The shape of the Mach cone and the associated diffusion wake are sensitive to the initial jet production location and the jet propagation direction relative to the radial flow because of the distortion by the collective expansion of the QGP and large density gradient. The shape of jet-induced Mach cones and their distortions in heavy-ion collisions provide a unique and direct probe of the dynamical evolution and the equation of state of QGP. However, it is difficult to identify the Mach cone and the diffusion wake in current experimental measurements of final hadron distributions because they are averaged over all possible initial jet production locations and propagation directions. To overcome this difficulty, we develop a deep learning assisted jet tomography which uses the full information of the final hadrons from jets to localize the initial jet production positions. This method can help to constrain the initial regions of jet production in heavy-ion collisions and enable a differential study of Mach-cones with different jet path length and orientation relative to the radial flow of the QGP in heavy-ion collisions

Gate-controlled non-volatile graphene-ferroelectric memory

In this letter, we demonstrate a non-volatile memory device in a graphene FET structure using ferroelectric gating. The binary information, i.e. "1" and "0", is represented by the high and low resistance states of the graphene working channels and is switched by controlling the polarization of the ferroelectric thin film using gate voltage sweep. A non-volatile resistance change exceeding 200% is achieved in our graphene-ferroelectric hybrid devices. The experimental observations are explained by the electrostatic doping of graphene by electric dipoles at the ferroelectric/graphene interface.Comment: 4 papes, 4 figure

Removal and Adsorption of p

In an attempt to explore the possibility of using carbon nanotubes (CNTs) as efficient adsorbents for removal of pollutants from the contaminated water, the adsorption of p-nitrophenol (PNP) on raw multiwalled carbon nanotubes (r.MWNTs) with different outer diameters, various functionalized multiwalled carbon nanotubes (f-MWNTs), raw single-walled carbon nanotubes (r.SWNTs) and oxidized single-walled carbon nanotubes (ox-SWNTs) has been investigated. The ox-SWNTs showed better adsorption ability for PNP with different concentrations, while lower uptake capacity was found for all of the r.MWNTs and f-MWNTs. The removal efficiency of PNP by ox-SWNTs was around 98%, indicating that ox-SWNTs possess a great potential application prospect for removing PNP from aqueous solutions